General-Purpose Robotic Navigation via LVLM-Orchestrated Perception, Reasoning, and Acting

• URL: http://arxiv.org/abs/2506.17462v1
• Date: Fri, 20 Jun 2025 20:06:14 GMT
• Title: General-Purpose Robotic Navigation via LVLM-Orchestrated Perception, Reasoning, and Acting
• Authors: Bernard Lange, Anil Yildiz, Mansur Arief, Shehryar Khattak, Mykel Kochenderfer, Georgios Georgakis,
• Abstract summary: Agentic Robotic Navigation Architecture (ARNA) is a general-purpose navigation framework that equips an LVLM-based agent with a library of perception, reasoning, and navigation tools.<n>At runtime, the agent autonomously defines and executes task-specific navigation that iteratively query the robotic modules, reason over multimodal inputs, and select appropriate navigation actions.<n>ARNA achieves state-of-the-art performance, demonstrating effective exploration, navigation, and embodied question answering without relying on handcrafted plans, fixed input representations, or pre-existing maps.
• Score: 9.157222032441531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing general-purpose navigation policies for unknown environments remains a core challenge in robotics. Most existing systems rely on task-specific neural networks and fixed data flows, limiting generalizability. Large Vision-Language Models (LVLMs) offer a promising alternative by embedding human-like knowledge suitable for reasoning and planning. Yet, prior LVLM-robot integrations typically depend on pre-mapped spaces, hard-coded representations, and myopic exploration. We introduce the Agentic Robotic Navigation Architecture (ARNA), a general-purpose navigation framework that equips an LVLM-based agent with a library of perception, reasoning, and navigation tools available within modern robotic stacks. At runtime, the agent autonomously defines and executes task-specific workflows that iteratively query the robotic modules, reason over multimodal inputs, and select appropriate navigation actions. This approach enables robust navigation and reasoning in previously unmapped environments, providing a new perspective on robotic stack design. Evaluated in Habitat Lab on the HM-EQA benchmark, ARNA achieves state-of-the-art performance, demonstrating effective exploration, navigation, and embodied question answering without relying on handcrafted plans, fixed input representations, or pre-existing maps.

Related papers

• Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities [65.98704516122228]
  The integration of foundation models (FMs) into robotics has enabled robots to understand natural language and reason about the semantics in their environments.<n>This paper addresses the deployment of FM-enabled robots in the field, where missions often require a robot to operate in large-scale and unstructured environments.<n>We present the first demonstration of large-scale LLM-enabled robot planning in unstructured environments with several kilometers of missions.
  arXiv  Detail & Related papers  (2025-05-14T15:28:43Z)
• NavigateDiff: Visual Predictors are Zero-Shot Navigation Assistants [24.689242976554482]
  Navigating unfamiliar environments presents significant challenges for household robots.<n>Existing reinforcement learning methods cannot be directly transferred to new environments.<n>We try to transfer the logical knowledge and the generalization ability of pre-trained foundation models to zero-shot navigation.
  arXiv  Detail & Related papers  (2025-02-19T17:27:47Z)
• Cognitive Planning for Object Goal Navigation using Generative AI Models [0.979851640406258]
  We present a novel framework for solving the object goal navigation problem that generates efficient exploration strategies. Our approach enables a robot to navigate unfamiliar environments by leveraging Large Language Models (LLMs) and Large Vision-Language Models (LVLMs)
  arXiv  Detail & Related papers  (2024-03-30T10:54:59Z)
• Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models [8.668211481067457]
  Co-NavGPT is a novel framework that integrates a Vision Language Model (VLM) as a global planner.<n>Co-NavGPT aggregates sub-maps from multiple robots with diverse viewpoints into a unified global map.<n>The VLM uses this information to assign frontiers across the robots, facilitating coordinated and efficient exploration.
  arXiv  Detail & Related papers  (2023-10-11T23:17:43Z)
• NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration [57.15811390835294]
  This paper describes how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration. We show that this unified policy results in better overall performance when navigating to visually indicated goals in novel environments. Our experiments, conducted on a real-world mobile robot platform, show effective navigation in unseen environments in comparison with five alternative methods.
  arXiv  Detail & Related papers  (2023-10-11T21:07:14Z)
• ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments [56.194988818341976]
  Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. We propose ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments. ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets.
  arXiv  Detail & Related papers  (2023-04-06T13:07:17Z)
• Can an Embodied Agent Find Your "Cat-shaped Mug"? LLM-Guided Exploration for Zero-Shot Object Navigation [58.3480730643517]
  We present LGX, a novel algorithm for Language-Driven Zero-Shot Object Goal Navigation (L-ZSON) Our approach makes use of Large Language Models (LLMs) for this task. We achieve state-of-the-art zero-shot object navigation results on RoboTHOR with a success rate (SR) improvement of over 27% over the current baseline.
  arXiv  Detail & Related papers  (2023-03-06T20:19:19Z)
• GNM: A General Navigation Model to Drive Any Robot [67.40225397212717]
  General goal-conditioned model for vision-based navigation can be trained on data obtained from many distinct but structurally similar robots. We analyze the necessary design decisions for effective data sharing across robots. We deploy the trained GNM on a range of new robots, including an under quadrotor.
  arXiv  Detail & Related papers  (2022-10-07T07:26:41Z)
• LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action [76.71101507291473]
  We present a system, LM-Nav, for robotic navigation that enjoys the benefits of training on unannotated large datasets of trajectories. We show that such a system can be constructed entirely out of pre-trained models for navigation (ViNG), image-language association (CLIP), and language modeling (GPT-3), without requiring any fine-tuning or language-annotated robot data.
  arXiv  Detail & Related papers  (2022-07-10T10:41:50Z)
• Learning Synthetic to Real Transfer for Localization and Navigational Tasks [7.019683407682642]
  Navigation is at the crossroad of multiple disciplines, it combines notions of computer vision, robotics and control. This work aimed at creating, in a simulation, a navigation pipeline whose transfer to the real world could be done with as few efforts as possible. To design the navigation pipeline four main challenges arise; environment, localization, navigation and planning.
  arXiv  Detail & Related papers  (2020-11-20T08:37:03Z)
• APPLD: Adaptive Planner Parameter Learning from Demonstration [48.63930323392909]
  We introduce APPLD, Adaptive Planner Learning from Demonstration, that allows existing navigation systems to be successfully applied to new complex environments. APPLD is verified on two robots running different navigation systems in different environments. Experimental results show that APPLD can outperform navigation systems with the default and expert-tuned parameters, and even the human demonstrator themselves.
  arXiv  Detail & Related papers  (2020-03-31T21:15:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.